calculateROCMeasures: Calculate receiver operator measures.

Description Usage Arguments Value Methods (by generic) See Also Examples

View source: R/calculateROCMeasures.R

Description

Calculate the absolute number of correct/incorrect classifications and the following evaluation measures:

For details on the used measures see measures and also https://en.wikipedia.org/wiki/Receiver_operating_characteristic.

The element for the false omission rate in the resulting object is not called for but fomr since for should never be used as a variable name in an object.

Usage

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calculateROCMeasures(pred)

## S3 method for class 'ROCMeasures'
print(x, abbreviations = TRUE, digits = 2, ...)

Arguments

pred

[Prediction]
Prediction object.

x

[ROCMeasures]
Created by calculateROCMeasures.

abbreviations

[logical(1)]
If TRUE a short paragraph with explanations of the used measures is printed additionally.

digits

[integer(1)]
Number of digits the measures are rounded to.

...

[any]
Currently not used.

Value

[ROCMeasures]. A list containing two elements confusion.matrix which is the 2 times 2 confusion matrix of absolute frequencies and measures, a list of the above mentioned measures.

Methods (by generic)

See Also

Other roc: asROCRPrediction, plotViperCharts

Other performance: ConfusionMatrix, calculateConfusionMatrix, estimateRelativeOverfitting, makeCostMeasure, makeCustomResampledMeasure, makeMeasure, measures, performance, setAggregation, setMeasurePars

Examples

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lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
calculateROCMeasures(pred)

berndbischl/mlr documentation built on Nov. 21, 2017, 12:51 a.m.